• DocumentCode
    2390768
  • Title

    Face recognition via robust face representation and compressive sensing

  • Author

    Shu, Xin ; Gao, Yao ; Lu, Hongtao

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Shanghai Jiaotong Univ., Shanghai, China
  • fYear
    2010
  • fDate
    6-8 Dec. 2010
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Many face recognition methods devote to the feature selection of training images while pay little efforts on the image representations. This paper proposes a robust face representation using the amplitude projection. The face recognition task can be solved using compressive sensing(CS) theory. First, the amplitude projections capture the horizontal distributions and the vertical distributions of the training faces. We form the projection image based on the horizontal and vertical distributions. Then robust face representation can be described as the input image combined with the projection image. Second, we fit the face recognition task into the compressive sensing framework. Due to the robust face representation and the excellent theory of CS, our approach gives better results when comparing with the state-of-art CS face recognition method. Experiments conducted on two well-known publicly face database verify the accuracy and efficiency of our approach.
  • Keywords
    face recognition; feature extraction; image representation; visual databases; amplitude projection; compressive sensing; face database; feature selection; horizontal distribution; image representation; projection image; robust face representation; state-of-art CS face recognition method; vertical distribution; Compressed sensing; Manganese; Principal component analysis; Time frequency analysis; World Wide Web;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Signal Processing and Communication Systems (ISPACS), 2010 International Symposium on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4244-7369-4
  • Type

    conf

  • DOI
    10.1109/ISPACS.2010.5704725
  • Filename
    5704725